Without validation, impact tolerances risk being theoretical, overly optimistic, or misaligned with real-world disruption dynamics.
Scenario-based calibration ensures that tolerances are realistic, evidence-based, and defensible. It enables organisations to simulate disruptions, observe how impacts evolve, and determine whether existing capabilities are sufficient to prevent breaches.
This chapter focuses on how to use structured scenarios—particularly Severe but Plausible Scenarios (SuPS)—to test, validate, and refine impact tolerances.
The purpose of this chapter is to:
Scenario-based calibration is anchored on the concept of Severe but Plausible Scenarios (SuPS).
A Severe but Plausible Scenario is:
A disruption event that is sufficiently severe to challenge the organisation’s resilience, yet credible based on its operating environment, historical incidents, and emerging risks.
Effective scenarios should be:
|
Characteristic |
Description |
|
Severe |
Capable of significantly disrupting critical services |
|
Plausible |
Realistic given organisational context and external environment |
|
Relevant |
Directly linked to the identified CBS and dependencies |
|
End-to-End |
Reflect a full service delivery chain, not isolated components |
|
Measurable |
Allow impact to be assessed against defined tolerance thresholds |
SuPS are used to:
Organisations should develop a diverse set of scenarios that reflect key risk domains.
Technology disruptions are among the most common and impactful scenarios.
Examples:
Impact Considerations:
Cyber incidents can escalate rapidly and affect multiple services simultaneously.
Examples:
Impact Considerations:
Modern organisations rely heavily on external providers.
Examples:
Impact Considerations:
People-related disruptions can significantly affect operational capacity.
Examples:
Impact Considerations:
A key component of scenario-based calibration is understanding how impact evolves.
Impact tolerance is not static—it reflects the point at which disruption becomes unacceptable.
|
Time Elapsed |
Service Condition |
Impact Level |
|
0–30 minutes |
Initial disruption, alerts triggered |
Low |
|
30 minutes–2 hours |
Transaction delays, increased customer enquiries |
Moderate |
|
2–4 hours |
Significant backlog, customer dissatisfaction |
High |
|
4–8 hours |
Potential tolerance breach, regulatory concern |
Very High |
|
8–24 hours |
Severe disruption, financial and reputational damage |
Extreme |
For each scenario, organisations should:
This enables calibration of whether the defined tolerance is:
Scenario testing should explicitly assess whether the organisation can remain within impact tolerance.
|
Scenario |
CBS |
Tolerance |
Observed Outcome |
Result |
|
Core Banking Outage |
Deposit Services |
4 hours MTD |
Recovery in 5 hours |
Breach |
|
Payment Gateway Failure |
Payments Services |
2 hours MTD |
Recovery in 1.5 hours |
Within Tolerance |
|
Cloud Provider Outage |
Digital Banking |
3 hours MTD |
Recovery in 4 hours |
Breach |
|
Pandemic Absenteeism |
Operations Processing |
70% capacity |
Operated at 60% |
Breach |
Scenario testing results should be used to refine impact tolerances.
|
Scenario Outcome |
Calibration Action |
|
Tolerance consistently breached |
Strengthen resilience capabilities or revise tolerance |
|
Tolerance nearly breached |
Enhance controls and monitoring |
|
Tolerance easily met |
Consider tightening the tolerance if appropriate |
|
Unrealistic tolerance identified |
Reassess based on practical capability |
Calibration must balance:
Scenario-based calibration is closely linked to Operational Resilience Phase 2 – Stage 4: Scenario Testing
|
Stage |
Role in Calibration |
|
Identify CBS |
Defines the scope of testing |
|
Map Dependencies |
Identifies failure points |
|
Set Impact Tolerance |
Defines thresholds |
|
Scenario Testing |
Validates and calibrates tolerances |
|
Improvement |
Drives remediation actions |
Scenario testing provides the evidence base for refining tolerances.
|
Challenge |
Description |
|
Overly simplistic scenarios |
Fails to capture real-world complexity |
|
Lack of data |
Difficulty quantifying impact accurately |
|
Siloed testing |
Does not reflect end-to-end service impact |
|
Underestimating interdependencies |
Misses cascading failures |
|
Unrealistic assumptions |
Leads to invalid conclusions |
|
Limited stakeholder involvement |
Results lack credibility |
To ensure effective calibration:
Scenario-based calibration is essential to ensure that impact tolerances are not merely theoretical but grounded in operational reality.
By applying Severe but Plausible Scenarios, organisations can test how disruptions unfold, understand how impact escalates over time, and determine whether they can truly operate within defined thresholds.
This process transforms impact tolerance from a static definition into a dynamic, evidence-based capability.
It enables organisations to identify weaknesses, prioritise improvements, and demonstrate to regulators that their resilience framework is both credible and effective.
In the next chapter, we will examine how to embed impact tolerance into governance, monitoring, and reporting, ensuring that it becomes an integral part of day-to-day resilience management rather than a one-time exercise.
| C1 | C2 | C3 | C4 | C5 | C6 |
| C7 | C8 | C9 | C10 | C11 | C12 |
| C13 | C14 | C15 | C16 | C17 | C18 |
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